Land-based or shallow marine seismic data acquisition and processing techniques are used to generate a profile (image) of a geophysical structure (subsurface) of the underlying strata. This profile does not necessarily provide an accurate location for oil and gas reservoirs, but it may suggest, to those trained in the field, the presence or absence of oil and/or gas reservoirs. Thus, providing an improved image of the subsurface in a shorter period of time is an ongoing process.
The estimation of residual static terms for a multi-component data set is a challenge. Estimating residual statics in two-dimensions and three-dimensions for P-P data or for converted waves such as P-S data is part of time or depth seismic processing. Conventional approaches of evaluating residual statics on P-P data and P-S data are predominantly based on linear inversion involving cross correlation functions as described by J. Ronen and J. F. Claerbout in their 1985 article entitled “Surface-Consistent Residual Statics Estimation by Stack Power Maximization” published in Geophysics and S. Jin, J. Li and S. Ronen in their 2004 paper entitled “Robust Inversion for Converted Wave Receiver Statics,” presented at the 74th Annual International Meeting of the Society of Exploration Geophysicists or trace-to-trace coherence of the common receiver stack section as described by P. W. Cary and W. S. Eaton in their 1993 article entitled “A Simple Method for Resolving Large Converted-Wave (P-Sv) Statics” published in Geophysics.
Further, it has been shown that the estimation of large-magnitude residual statics is better handled with a non-linear system, as described by Daniel H. Rothman in his 1985 article entitled “Non-linear Inversion, Statistical Mechanics and Residual Statics Estimation” and his 1986 article entitled “Automatic Estimation of Large Residual Statics Corrections” both published in Geophysics, based on a Monte Carlo method, as described by D. Le Meur and S. Merrer in their paper entitled “Monte Carlo Statics: The Last Frontier” presented at the 2004 Canadian Society of Exploration Geophysicists Annual Convention, and coupled with a simulated annealing approach, as described by K. Vasudevan, W. G. Wilson and W. G. Laidlaw in their 1991 article entitled “Simulated Annealing Statics Computation Using an Order-based Energy Function” published in Geophysics. Accordingly, in converted waves processing, shear (transverse) wave receiver statics are characterized by a large magnitude which can be two to ten times greater than P-P static values as well as by noisier input data than the P-P data.
It is known in the art that different methods are used for computing surface-consistent residual statics on P-P data and receiver statics on P-S data as described by D. Marsden in his 1993 article entitled “Static Corrections—A Review” published in The Leading Edge. Most of these methods are based on the use of cross-correlation functions and a solution of a system of linear equations, which very frequently give a local minimum solution based on the nature of the data. A non-linear approach, however, using the simulated annealing concept, as described by S. Kirkpatrick, C. D. Gelatt, Jr. and M. P. Vehhi in their 1983 article entitled “Optimization by Simulated Annealing” published in Science, coupled with a Monte Carlo technique, as described by N. Metropolis, A. Rosenbluth, M. Rosenbluth, A. Teller and E. Teller in their 1953 article entitled “Equation of State Calculations by Fast Computing Machines” published in the Journal of Chemistry and Physics, allows computing any type of residual statics at the global minimum.
Looking now to background art FIG. 1, a flowchart 100 for estimating P-P residual statics 110, 112 and P-S residual statics 116 is depicted. The method is a two-pass calculation with the first pass 102 operating on the P-P data 106 and the second pass 104 operating on P-S data 108 and the P-P source residual statics 110 output from the first pass 102. The first pass 102 is dedicated to calculating P-P source residual statics 110 and P-P receiver residual statics 112 by applying a non-linear statics solver 114 to the P-P data 106. The second pass 104 is dedicated to calculating the P-S residual statics 116 by applying the P-P source residual statics 110 to the P-S data 108 and then applying the non-linear statics solver 114 to the updated P-S data 108. The two-pass nature of this approach leads to undesirably long computational times based on the data access mechanisms for the large volume of data associated with the P-P data 106 and the P-S data 108.
Accordingly, it would be desirable to provide systems and methods that avoid the afore-described problems and drawbacks, and improve the accuracy of the final images which are produced as a result of such seismic surveys.